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Research Topic

MAPPING: MAnagement and Processing of Images for Population ImagiNG

About this Research Topic

Several recent papers underline methodological points that limit the validity of published results in life science and especially in neurosciences. At least three main points are emphasized that lead to invalidated findings: the endemic low statistical power of the published studies due to the small size of ...

Several recent papers underline methodological points that limit the validity of published results in life science and especially in neurosciences. At least three main points are emphasized that lead to invalidated findings: the endemic low statistical power of the published studies due to the small size of the population involved, data analysis and reporting are often selective and biased, and studies are rarely replicated then false discoveries or solutions persist. To overcome the poor reliability of research finding, several actions should be promoted including large cohort studies, data sharing and data re-analysis. Large-scale online databases may contribute to the definition of a “collective mind” facilitating open collaborative work or “crowd science”. Although technology alone cannot change scientist’s practices, technical solutions should be identified and implemented allowing effective population imaging and data sharing without increasing the burden on researchers. This research topic (RT), cross-listed between the speciality section Computer Image Analysis of Frontiers in ICT and Frontiers in Neuroinformatics, is devoted to the methodological aspects and existing solutions to support the constitution and the management of large cohorts and facilitate data sharing between distributed data repositories in life science domain. Moreover, specific links to dedicated software and hardware infrastructures should be developed for the sharing and execution of image processing workflows making easier replication and comparison of data analysis procedures. This RT emphasizes the main conceptual and technical challenges posed by population imaging at a large scale: in brief, do we need standards for data model as provided by domain and application ontologies? How to interoperate between distributed repositories? Which levels of quality control (manual or automatic) and data provenance are required? Are big data centers preferable to federated distributed systems? What are the current solutions for high performance computing for in vivo imaging large repositories? Should we develop generic or tailored to usages solutions? Are cloud-computing clusters better solutions than grids or crowd computing? Is image processing pipelines sharing different of data sharing?

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